Mohammad Zohaib

I am a postdoctoral researcher at PAVIS, Istituto Italiano di Tecnologia (IIT), Genova, Italy, working with Dr. Alessio Del Bue. I am working on a research problem, estimating the 3D keypoints for deformable objects in a self-supervised way.

I have done my PhD from the same (PAVIS) lab on March 27th, 2023, where I have desinged deep leanring networks to estimate 3D keypoints for rigid objects considering 2D and 3D data.

my_page@IIT / LinkedIn / Google Scholar / Github / Google-site / Email

Selected Publications

	SC3K: Self-supervised and Coherent 3D Keypoints Estimation from Rotated, Noisy, and Decimated Point Cloud Data Mohammad Zohaib, Alessio Del Bue ICCV, 2023 PDF / Code The paper presents a self-supervised approach to estimate 3D keypoints that are semantically coherent, proximal to PCD surface and resilient to PCD perturbations.
	CDHN: Cross-Domain Hallucination Network For 3D Key-points Estimation”, Pattern Recognition Mohammad Zohaib, Milind G. Padalkar, Pietro Morerio, Matteo Taiana, Alessio Del Bue Pattern Recognition, 2023 , Under review PDF The paper presents a teacher-student network that hallucinates 3D features directly from RGB images to improve the keypoints localization.
	A featureless approach for object detection and tracking in dynamic environments Mohammad Zohaib, Muhammad Ahsan Mudassir Khan Jamshed Iqbal Public Library of Science (PLoS) ONE journal, 2023 PDF / This work presents a ROS-based algorithm for constructing dynamic maps, which exploits the spatial-temporal locality for detecting and tracking moving objects without relying on prior knowledge of their geometrical features.
	3D Key-points Estimation From Single-view RGB Images Mohammad Zohaib, Matteo Taiana, Milind G. Padalkar, Alessio Del Bue ICIAP, 2022 [Oral presentation] PDF / Sup. Material / Video Our paper presents an end-to-end approach that leverages single-view RGB images for estimating an ordered list of 3D keypoints.
	Towards Reconstruction of 3D Shapes in a Realistic Environment Mohammad Zohaib, Matteo Taiana, Alessio Del Bue ICIAP, 2022 PDF / Sup. Material / Poster / Video Our paper presents an end-to-end 3D reconsturction approach that learns to compute stable features for an object by reducing the influence of image background.
	An Autonomous Seeder for Maize Crop Amir Asghar Ali, Mohammad Zohaib, Syed Atif Mehdi ICRAI, 2019 PDF The paper presents an autonomous seeder robot that minimizes human involvement in the sowing process; thus, it reduces errors and time consumption and increases efficiency.

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